Overview

Dataset statistics

Number of variables21
Number of observations2176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory285.0 KiB
Average record size in memory134.1 B

Variable types

Text1
Categorical11
Numeric7
DateTime1
Boolean1

Alerts

current_price is highly overall correlated with original_priceHigh correlation
customer_rating is highly overall correlated with rating_categoryHigh correlation
day is highly overall correlated with season and 1 other fieldsHigh correlation
day_name is highly overall correlated with season and 1 other fieldsHigh correlation
discount_flag is highly overall correlated with markdown_percentageHigh correlation
is_returned is highly overall correlated with return_reasonHigh correlation
markdown_percentage is highly overall correlated with discount_flagHigh correlation
month is highly overall correlated with season and 1 other fieldsHigh correlation
original_price is highly overall correlated with current_priceHigh correlation
rating_category is highly overall correlated with customer_ratingHigh correlation
return_reason is highly overall correlated with is_returnedHigh correlation
season is highly overall correlated with day and 3 other fieldsHigh correlation
year is highly overall correlated with day and 3 other fieldsHigh correlation
return_reason is highly imbalanced (65.1%)Imbalance
year is highly imbalanced (52.9%)Imbalance
day_name is highly imbalanced (54.5%)Imbalance
out_of_stock is highly imbalanced (86.0%)Imbalance
product_id has unique valuesUnique
markdown_percentage has 1373 (63.1%) zerosZeros
stock_quantity has 43 (2.0%) zerosZeros

Reproduction

Analysis started2026-01-23 06:52:18.702428
Analysis finished2026-01-23 06:52:41.596523
Duration22.89 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

product_id
Text

Unique 

Distinct2176
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:42.036206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17408
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2176 ?
Unique (%)100.0%

Sample

1st rowFB000001
2nd rowFB000002
3rd rowFB000003
4th rowFB000004
5th rowFB000005
ValueCountFrequency (%)
fb0000011
 
< 0.1%
fb0000101
 
< 0.1%
fb0000161
 
< 0.1%
fb0000151
 
< 0.1%
fb0000141
 
< 0.1%
fb0000131
 
< 0.1%
fb0000121
 
< 0.1%
fb0000111
 
< 0.1%
fb0000091
 
< 0.1%
fb0014541
 
< 0.1%
Other values (2166)2166
99.5%
2026-01-23T13:52:42.876211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
06086
35.0%
F2176
 
12.5%
B2176
 
12.5%
11715
 
9.9%
2815
 
4.7%
4638
 
3.7%
5638
 
3.7%
3638
 
3.7%
6638
 
3.7%
7634
 
3.6%
Other values (2)1254
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)17408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06086
35.0%
F2176
 
12.5%
B2176
 
12.5%
11715
 
9.9%
2815
 
4.7%
4638
 
3.7%
5638
 
3.7%
3638
 
3.7%
6638
 
3.7%
7634
 
3.6%
Other values (2)1254
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06086
35.0%
F2176
 
12.5%
B2176
 
12.5%
11715
 
9.9%
2815
 
4.7%
4638
 
3.7%
5638
 
3.7%
3638
 
3.7%
6638
 
3.7%
7634
 
3.6%
Other values (2)1254
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06086
35.0%
F2176
 
12.5%
B2176
 
12.5%
11715
 
9.9%
2815
 
4.7%
4638
 
3.7%
5638
 
3.7%
3638
 
3.7%
6638
 
3.7%
7634
 
3.6%
Other values (2)1254
 
7.2%

category
Categorical

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Accessories
402 
Bottoms
392 
Shoes
372 
Tops
359 
Outerwear
334 

Length

Max length11
Median length9
Mean length7.2090993
Min length4

Characters and Unicode

Total characters15687
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOuterwear
2nd rowTops
3rd rowAccessories
4th rowShoes
5th rowTops

Common Values

ValueCountFrequency (%)
Accessories402
18.5%
Bottoms392
18.0%
Shoes372
17.1%
Tops359
16.5%
Outerwear334
15.3%
Dresses317
14.6%

Length

2026-01-23T13:52:43.167149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:43.696072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
accessories402
18.5%
bottoms392
18.0%
shoes372
17.1%
tops359
16.5%
outerwear334
15.3%
dresses317
14.6%

Most occurring characters

ValueCountFrequency (%)
s3280
20.9%
e2478
15.8%
o1917
12.2%
r1387
8.8%
t1118
 
7.1%
c804
 
5.1%
A402
 
2.6%
i402
 
2.6%
m392
 
2.5%
B392
 
2.5%
Other values (9)3115
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)15687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s3280
20.9%
e2478
15.8%
o1917
12.2%
r1387
8.8%
t1118
 
7.1%
c804
 
5.1%
A402
 
2.6%
i402
 
2.6%
m392
 
2.5%
B392
 
2.5%
Other values (9)3115
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s3280
20.9%
e2478
15.8%
o1917
12.2%
r1387
8.8%
t1118
 
7.1%
c804
 
5.1%
A402
 
2.6%
i402
 
2.6%
m392
 
2.5%
B392
 
2.5%
Other values (9)3115
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s3280
20.9%
e2478
15.8%
o1917
12.2%
r1387
8.8%
t1118
 
7.1%
c804
 
5.1%
A402
 
2.6%
i402
 
2.6%
m392
 
2.5%
B392
 
2.5%
Other values (9)3115
19.9%

brand
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Zara
313 
Banana Republic
285 
Mango
284 
H&M
283 
Uniqlo
260 
Other values (3)
751 

Length

Max length15
Median length9
Mean length6.8511029
Min length3

Characters and Unicode

Total characters14908
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZara
2nd rowUniqlo
3rd rowUniqlo
4th rowUniqlo
5th rowBanana Republic

Common Values

ValueCountFrequency (%)
Zara313
14.4%
Banana Republic285
13.1%
Mango284
13.1%
H&M283
13.0%
Uniqlo260
11.9%
Ann Taylor257
11.8%
Forever21250
11.5%
Gap244
11.2%

Length

2026-01-23T13:52:44.816405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:45.124171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
zara313
11.5%
banana285
10.5%
republic285
10.5%
mango284
10.4%
h&m283
10.4%
uniqlo260
9.6%
ann257
9.5%
taylor257
9.5%
forever21250
9.2%
gap244
9.0%

Most occurring characters

ValueCountFrequency (%)
a2266
15.2%
n1628
 
10.9%
r1070
 
7.2%
o1051
 
7.0%
l802
 
5.4%
e785
 
5.3%
M567
 
3.8%
i545
 
3.7%
542
 
3.6%
p529
 
3.5%
Other values (19)5123
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2266
15.2%
n1628
 
10.9%
r1070
 
7.2%
o1051
 
7.0%
l802
 
5.4%
e785
 
5.3%
M567
 
3.8%
i545
 
3.7%
542
 
3.6%
p529
 
3.5%
Other values (19)5123
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2266
15.2%
n1628
 
10.9%
r1070
 
7.2%
o1051
 
7.0%
l802
 
5.4%
e785
 
5.3%
M567
 
3.8%
i545
 
3.7%
542
 
3.6%
p529
 
3.5%
Other values (19)5123
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2266
15.2%
n1628
 
10.9%
r1070
 
7.2%
o1051
 
7.0%
l802
 
5.4%
e785
 
5.3%
M567
 
3.8%
i545
 
3.7%
542
 
3.6%
p529
 
3.5%
Other values (19)5123
34.4%

season
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Summer
575 
Fall
544 
Spring
535 
Winter
522 

Length

Max length6
Median length6
Mean length5.5
Min length4

Characters and Unicode

Total characters11968
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowWinter
3rd rowWinter
4th rowSpring
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer575
26.4%
Fall544
25.0%
Spring535
24.6%
Winter522
24.0%

Length

2026-01-23T13:52:45.585567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:45.865267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
summer575
26.4%
fall544
25.0%
spring535
24.6%
winter522
24.0%

Most occurring characters

ValueCountFrequency (%)
r1632
13.6%
m1150
9.6%
S1110
9.3%
e1097
9.2%
l1088
9.1%
i1057
8.8%
n1057
8.8%
u575
 
4.8%
F544
 
4.5%
a544
 
4.5%
Other values (4)2114
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)11968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1632
13.6%
m1150
9.6%
S1110
9.3%
e1097
9.2%
l1088
9.1%
i1057
8.8%
n1057
8.8%
u575
 
4.8%
F544
 
4.5%
a544
 
4.5%
Other values (4)2114
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1632
13.6%
m1150
9.6%
S1110
9.3%
e1097
9.2%
l1088
9.1%
i1057
8.8%
n1057
8.8%
u575
 
4.8%
F544
 
4.5%
a544
 
4.5%
Other values (4)2114
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1632
13.6%
m1150
9.6%
S1110
9.3%
e1097
9.2%
l1088
9.1%
i1057
8.8%
n1057
8.8%
u575
 
4.8%
F544
 
4.5%
a544
 
4.5%
Other values (4)2114
17.7%

size
Categorical

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Unknown
491 
XS
301 
S
294 
XL
283 
XXL
279 
Other values (2)
528 

Length

Max length7
Median length3
Mean length2.8786765
Min length1

Characters and Unicode

Total characters6264
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXL
2nd rowL
3rd rowUnknown
4th rowXL
5th rowXL

Common Values

ValueCountFrequency (%)
Unknown491
22.6%
XS301
13.8%
S294
13.5%
XL283
13.0%
XXL279
12.8%
L273
12.5%
M255
11.7%

Length

2026-01-23T13:52:46.253338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:46.525247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown491
22.6%
xs301
13.8%
s294
13.5%
xl283
13.0%
xxl279
12.8%
l273
12.5%
m255
11.7%

Most occurring characters

ValueCountFrequency (%)
n1473
23.5%
X1142
18.2%
L835
13.3%
S595
9.5%
U491
 
7.8%
k491
 
7.8%
o491
 
7.8%
w491
 
7.8%
M255
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1473
23.5%
X1142
18.2%
L835
13.3%
S595
9.5%
U491
 
7.8%
k491
 
7.8%
o491
 
7.8%
w491
 
7.8%
M255
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1473
23.5%
X1142
18.2%
L835
13.3%
S595
9.5%
U491
 
7.8%
k491
 
7.8%
o491
 
7.8%
w491
 
7.8%
M255
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1473
23.5%
X1142
18.2%
L835
13.3%
S595
9.5%
U491
 
7.8%
k491
 
7.8%
o491
 
7.8%
w491
 
7.8%
M255
 
4.1%

color
Categorical

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Green
222 
Black
216 
Red
212 
White
207 
Pink
204 
Other values (6)
1115 

Length

Max length6
Median length5
Mean length4.5344669
Min length3

Characters and Unicode

Total characters9867
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed
2nd rowPink
3rd rowBlack
4th rowBlack
5th rowBlack

Common Values

ValueCountFrequency (%)
Green222
10.2%
Black216
9.9%
Red212
9.7%
White207
9.5%
Pink204
9.4%
Navy192
8.8%
Blue191
8.8%
Gray189
8.7%
Purple187
8.6%
Brown180
8.3%

Length

2026-01-23T13:52:46.828894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
green222
10.2%
black216
9.9%
red212
9.7%
white207
9.5%
pink204
9.4%
navy192
8.8%
blue191
8.8%
gray189
8.7%
purple187
8.6%
brown180
8.3%

Most occurring characters

ValueCountFrequency (%)
e1593
16.1%
r778
 
7.9%
B763
 
7.7%
n606
 
6.1%
a597
 
6.1%
l594
 
6.0%
i587
 
5.9%
k420
 
4.3%
G411
 
4.2%
P391
 
4.0%
Other values (14)3127
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)9867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1593
16.1%
r778
 
7.9%
B763
 
7.7%
n606
 
6.1%
a597
 
6.1%
l594
 
6.0%
i587
 
5.9%
k420
 
4.3%
G411
 
4.2%
P391
 
4.0%
Other values (14)3127
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1593
16.1%
r778
 
7.9%
B763
 
7.7%
n606
 
6.1%
a597
 
6.1%
l594
 
6.0%
i587
 
5.9%
k420
 
4.3%
G411
 
4.2%
P391
 
4.0%
Other values (14)3127
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1593
16.1%
r778
 
7.9%
B763
 
7.7%
n606
 
6.1%
a597
 
6.1%
l594
 
6.0%
i587
 
5.9%
k420
 
4.3%
G411
 
4.2%
P391
 
4.0%
Other values (14)3127
31.7%

original_price
Real number (ℝ)

High correlation 

Distinct2061
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.20034
Minimum15.14
Maximum249.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:47.093711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.14
5-th percentile28.7875
Q157.8725
median88.6
Q3127.63
95-th percentile198.1525
Maximum249.98
Range234.84
Interquartile range (IQR)69.7575

Descriptive statistics

Standard deviation51.637345
Coefficient of variation (CV)0.53124655
Kurtosis-0.06816943
Mean97.20034
Median Absolute Deviation (MAD)33.745
Skewness0.72249313
Sum211507.94
Variance2666.4154
MonotonicityNot monotonic
2026-01-23T13:52:47.386249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.033
 
0.1%
115.893
 
0.1%
69.542
 
0.1%
184.872
 
0.1%
119.782
 
0.1%
159.252
 
0.1%
74.542
 
0.1%
109.152
 
0.1%
59.12
 
0.1%
155.852
 
0.1%
Other values (2051)2154
99.0%
ValueCountFrequency (%)
15.141
< 0.1%
15.361
< 0.1%
15.711
< 0.1%
15.921
< 0.1%
16.511
< 0.1%
16.521
< 0.1%
16.541
< 0.1%
16.571
< 0.1%
16.641
< 0.1%
16.811
< 0.1%
ValueCountFrequency (%)
249.981
< 0.1%
248.581
< 0.1%
248.411
< 0.1%
248.051
< 0.1%
247.91
< 0.1%
246.651
< 0.1%
246.491
< 0.1%
245.871
< 0.1%
245.751
< 0.1%
245.311
< 0.1%

markdown_percentage
Real number (ℝ)

High correlation  Zeros 

Distinct439
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.547794
Minimum0
Maximum59.8
Zeros1373
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:47.717941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.65
95-th percentile50.625
Maximum59.8
Range59.8
Interquartile range (IQR)17.65

Descriptive statistics

Standard deviation17.272466
Coefficient of variation (CV)1.637543
Kurtosis0.77336564
Mean10.547794
Median Absolute Deviation (MAD)0
Skewness1.4655079
Sum22952
Variance298.33809
MonotonicityNot monotonic
2026-01-23T13:52:48.017425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01373
63.1%
8.86
 
0.3%
56.45
 
0.2%
24.35
 
0.2%
185
 
0.2%
42.25
 
0.2%
6.65
 
0.2%
49.45
 
0.2%
15.84
 
0.2%
35.34
 
0.2%
Other values (429)759
34.9%
ValueCountFrequency (%)
01373
63.1%
0.32
 
0.1%
0.41
 
< 0.1%
0.51
 
< 0.1%
0.71
 
< 0.1%
0.81
 
< 0.1%
0.92
 
0.1%
11
 
< 0.1%
1.14
 
0.2%
1.22
 
0.1%
ValueCountFrequency (%)
59.83
0.1%
59.72
0.1%
59.53
0.1%
59.41
 
< 0.1%
59.31
 
< 0.1%
59.11
 
< 0.1%
58.81
 
< 0.1%
58.71
 
< 0.1%
58.62
0.1%
58.53
0.1%

current_price
Real number (ℝ)

High correlation 

Distinct2025
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.499596
Minimum7.29
Maximum249.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:48.273421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.29
5-th percentile23.3575
Q146.845
median74.885
Q3112.865
95-th percentile183.075
Maximum249.98
Range242.69
Interquartile range (IQR)66.02

Descriptive statistics

Standard deviation50.091354
Coefficient of variation (CV)0.58586656
Kurtosis0.31233393
Mean85.499596
Median Absolute Deviation (MAD)32.11
Skewness0.89336326
Sum186047.12
Variance2509.1437
MonotonicityNot monotonic
2026-01-23T13:52:48.631169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.543
 
0.1%
88.943
 
0.1%
74.773
 
0.1%
33.772
 
0.1%
52.072
 
0.1%
99.262
 
0.1%
169.892
 
0.1%
90.972
 
0.1%
92.252
 
0.1%
23.042
 
0.1%
Other values (2015)2153
98.9%
ValueCountFrequency (%)
7.291
< 0.1%
8.231
< 0.1%
9.581
< 0.1%
9.711
< 0.1%
10.481
< 0.1%
10.551
< 0.1%
10.681
< 0.1%
11.161
< 0.1%
11.291
< 0.1%
11.641
< 0.1%
ValueCountFrequency (%)
249.981
< 0.1%
248.581
< 0.1%
248.051
< 0.1%
246.651
< 0.1%
246.491
< 0.1%
245.871
< 0.1%
245.751
< 0.1%
244.131
< 0.1%
241.811
< 0.1%
241.091
< 0.1%
Distinct273
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Minimum2024-08-06 00:00:00
Maximum2025-08-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-23T13:52:48.972131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:49.223406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stock_quantity
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.93796
Minimum0
Maximum50
Zeros43
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:49.499703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median25
Q337
95-th percentile47
Maximum50
Range50
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.451387
Coefficient of variation (CV)0.57949356
Kurtosis-1.1829594
Mean24.93796
Median Absolute Deviation (MAD)12
Skewness-0.0061644575
Sum54265
Variance208.84259
MonotonicityNot monotonic
2026-01-23T13:52:49.776046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4352
 
2.4%
3352
 
2.4%
3651
 
2.3%
1651
 
2.3%
1750
 
2.3%
950
 
2.3%
2449
 
2.3%
4149
 
2.3%
2749
 
2.3%
3448
 
2.2%
Other values (41)1675
77.0%
ValueCountFrequency (%)
043
2.0%
134
1.6%
240
1.8%
334
1.6%
446
2.1%
547
2.2%
642
1.9%
748
2.2%
832
1.5%
950
2.3%
ValueCountFrequency (%)
5042
1.9%
4932
1.5%
4830
1.4%
4744
2.0%
4644
2.0%
4535
1.6%
4435
1.6%
4352
2.4%
4245
2.1%
4149
2.3%

customer_rating
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9866042
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2026-01-23T13:52:50.104584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q12.2
median2.9866042
Q33.8
95-th percentile4.7
Maximum5
Range4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0588227
Coefficient of variation (CV)0.35452394
Kurtosis-0.83116789
Mean2.9866042
Median Absolute Deviation (MAD)0.78660419
Skewness-0.0028926259
Sum6498.8507
Variance1.1211055
MonotonicityNot monotonic
2026-01-23T13:52:50.441892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2.98660419362
 
16.6%
2.561
 
2.8%
456
 
2.6%
1.255
 
2.5%
3.153
 
2.4%
1.152
 
2.4%
2.852
 
2.4%
4.551
 
2.3%
4.650
 
2.3%
3.550
 
2.3%
Other values (32)1334
61.3%
ValueCountFrequency (%)
122
 
1.0%
1.152
2.4%
1.255
2.5%
1.348
2.2%
1.447
2.2%
1.539
1.8%
1.642
1.9%
1.747
2.2%
1.845
2.1%
1.941
1.9%
ValueCountFrequency (%)
524
1.1%
4.942
1.9%
4.837
1.7%
4.745
2.1%
4.650
2.3%
4.551
2.3%
4.447
2.2%
4.347
2.2%
4.234
1.6%
4.150
2.3%

is_returned
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
1856 
True
320 
ValueCountFrequency (%)
False1856
85.3%
True320
 
14.7%
2026-01-23T13:52:50.672187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

return_reason
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
No Return
1856 
Changed Mind
 
68
Size Issue
 
60
Quality Issue
 
55
Wrong Item
 
47
Other values (2)
 
90

Length

Max length14
Median length9
Mean length9.3092831
Min length7

Characters and Unicode

Total characters20257
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Return
2nd rowNo Return
3rd rowNo Return
4th rowNo Return
5th rowNo Return

Common Values

ValueCountFrequency (%)
No Return1856
85.3%
Changed Mind68
 
3.1%
Size Issue60
 
2.8%
Quality Issue55
 
2.5%
Wrong Item47
 
2.2%
Color Mismatch46
 
2.1%
Damaged44
 
2.0%

Length

2026-01-23T13:52:50.897569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:51.117103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no1856
43.1%
return1856
43.1%
issue115
 
2.7%
changed68
 
1.6%
mind68
 
1.6%
size60
 
1.4%
quality55
 
1.3%
wrong47
 
1.1%
item47
 
1.1%
color46
 
1.1%
Other values (2)90
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e2190
10.8%
2132
10.5%
n2039
10.1%
u2026
10.0%
t2004
9.9%
o1995
9.8%
r1949
9.6%
N1856
9.2%
R1856
9.2%
s276
 
1.4%
Other values (17)1934
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)20257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2190
10.8%
2132
10.5%
n2039
10.1%
u2026
10.0%
t2004
9.9%
o1995
9.8%
r1949
9.6%
N1856
9.2%
R1856
9.2%
s276
 
1.4%
Other values (17)1934
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2190
10.8%
2132
10.5%
n2039
10.1%
u2026
10.0%
t2004
9.9%
o1995
9.8%
r1949
9.6%
N1856
9.2%
R1856
9.2%
s276
 
1.4%
Other values (17)1934
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2190
10.8%
2132
10.5%
n2039
10.1%
u2026
10.0%
t2004
9.9%
o1995
9.8%
r1949
9.6%
N1856
9.2%
R1856
9.2%
s276
 
1.4%
Other values (17)1934
9.5%

year
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
2025
1957 
2024
219 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8704
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025
2nd row2025
3rd row2025
4th row2025
5th row2025

Common Values

ValueCountFrequency (%)
20251957
89.9%
2024219
 
10.1%

Length

2026-01-23T13:52:51.407944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:51.555895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20251957
89.9%
2024219
 
10.1%

Most occurring characters

ValueCountFrequency (%)
24352
50.0%
02176
25.0%
51957
22.5%
4219
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24352
50.0%
02176
25.0%
51957
22.5%
4219
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24352
50.0%
02176
25.0%
51957
22.5%
4219
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24352
50.0%
02176
25.0%
51957
22.5%
4219
 
2.5%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6461397
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2026-01-23T13:52:51.679068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7782243
Coefficient of variation (CV)0.23256498
Kurtosis4.871438
Mean7.6461397
Median Absolute Deviation (MAD)0
Skewness-1.711049
Sum16638
Variance3.1620816
MonotonicityNot monotonic
2026-01-23T13:52:51.864414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
81681
77.3%
958
 
2.7%
457
 
2.6%
754
 
2.5%
548
 
2.2%
1046
 
2.1%
341
 
1.9%
141
 
1.9%
1241
 
1.9%
1140
 
1.8%
Other values (2)69
 
3.2%
ValueCountFrequency (%)
141
 
1.9%
237
 
1.7%
341
 
1.9%
457
 
2.6%
548
 
2.2%
632
 
1.5%
754
 
2.5%
81681
77.3%
958
 
2.7%
1046
 
2.1%
ValueCountFrequency (%)
1241
 
1.9%
1140
 
1.8%
1046
 
2.1%
958
 
2.7%
81681
77.3%
754
 
2.5%
632
 
1.5%
548
 
2.2%
457
 
2.6%
341
 
1.9%

day
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.457261
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2026-01-23T13:52:52.075938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median6
Q36
95-th percentile25
Maximum31
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.1207026
Coefficient of variation (CV)0.72372161
Kurtosis3.9415628
Mean8.457261
Median Absolute Deviation (MAD)0
Skewness2.2573703
Sum18403
Variance37.463
MonotonicityNot monotonic
2026-01-23T13:52:52.297257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
61658
76.2%
2726
 
1.2%
1625
 
1.1%
1723
 
1.1%
1022
 
1.0%
2322
 
1.0%
2521
 
1.0%
220
 
0.9%
320
 
0.9%
1220
 
0.9%
Other values (21)319
 
14.7%
ValueCountFrequency (%)
113
 
0.6%
220
 
0.9%
320
 
0.9%
414
 
0.6%
515
 
0.7%
61658
76.2%
719
 
0.9%
816
 
0.7%
918
 
0.8%
1022
 
1.0%
ValueCountFrequency (%)
319
 
0.4%
3013
0.6%
2917
0.8%
2819
0.9%
2726
1.2%
2620
0.9%
2521
1.0%
2417
0.8%
2322
1.0%
2211
0.5%

day_name
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Wednesday
1720 
Friday
 
100
Thursday
 
81
Tuesday
 
79
Saturday
 
76
Other values (2)
 
120

Length

Max length9
Median length9
Mean length8.5519301
Min length6

Characters and Unicode

Total characters18609
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowWednesday
3rd rowWednesday
4th rowMonday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Wednesday1720
79.0%
Friday100
 
4.6%
Thursday81
 
3.7%
Tuesday79
 
3.6%
Saturday76
 
3.5%
Monday68
 
3.1%
Sunday52
 
2.4%

Length

2026-01-23T13:52:52.596328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:52.842502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wednesday1720
79.0%
friday100
 
4.6%
thursday81
 
3.7%
tuesday79
 
3.6%
saturday76
 
3.5%
monday68
 
3.1%
sunday52
 
2.4%

Most occurring characters

ValueCountFrequency (%)
d3896
20.9%
e3519
18.9%
a2252
12.1%
y2176
11.7%
s1880
10.1%
n1840
9.9%
W1720
9.2%
u288
 
1.5%
r257
 
1.4%
T160
 
0.9%
Other values (7)621
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)18609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d3896
20.9%
e3519
18.9%
a2252
12.1%
y2176
11.7%
s1880
10.1%
n1840
9.9%
W1720
9.2%
u288
 
1.5%
r257
 
1.4%
T160
 
0.9%
Other values (7)621
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d3896
20.9%
e3519
18.9%
a2252
12.1%
y2176
11.7%
s1880
10.1%
n1840
9.9%
W1720
9.2%
u288
 
1.5%
r257
 
1.4%
T160
 
0.9%
Other values (7)621
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d3896
20.9%
e3519
18.9%
a2252
12.1%
y2176
11.7%
s1880
10.1%
n1840
9.9%
W1720
9.2%
u288
 
1.5%
r257
 
1.4%
T160
 
0.9%
Other values (7)621
 
3.3%

out_of_stock
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
0
2133 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

Length

2026-01-23T13:52:53.137984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:53.318665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

Most occurring characters

ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02133
98.0%
143
 
2.0%

discount_flag
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
0
1373 
1
803 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01373
63.1%
1803
36.9%

Length

2026-01-23T13:52:53.543750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:53.727849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01373
63.1%
1803
36.9%

Most occurring characters

ValueCountFrequency (%)
01373
63.1%
1803
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01373
63.1%
1803
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01373
63.1%
1803
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01373
63.1%
1803
36.9%

rating_category
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Medium
1267 
Low
482 
High
427 

Length

Max length6
Median length6
Mean length4.9430147
Min length3

Characters and Unicode

Total characters10756
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowHigh
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium1267
58.2%
Low482
 
22.2%
High427
 
19.6%

Length

2026-01-23T13:52:54.295612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T13:52:54.461151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium1267
58.2%
low482
 
22.2%
high427
 
19.6%

Most occurring characters

ValueCountFrequency (%)
i1694
15.7%
M1267
11.8%
e1267
11.8%
d1267
11.8%
u1267
11.8%
m1267
11.8%
L482
 
4.5%
o482
 
4.5%
w482
 
4.5%
H427
 
4.0%
Other values (2)854
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1694
15.7%
M1267
11.8%
e1267
11.8%
d1267
11.8%
u1267
11.8%
m1267
11.8%
L482
 
4.5%
o482
 
4.5%
w482
 
4.5%
H427
 
4.0%
Other values (2)854
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1694
15.7%
M1267
11.8%
e1267
11.8%
d1267
11.8%
u1267
11.8%
m1267
11.8%
L482
 
4.5%
o482
 
4.5%
w482
 
4.5%
H427
 
4.0%
Other values (2)854
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1694
15.7%
M1267
11.8%
e1267
11.8%
d1267
11.8%
u1267
11.8%
m1267
11.8%
L482
 
4.5%
o482
 
4.5%
w482
 
4.5%
H427
 
4.0%
Other values (2)854
7.9%

Interactions

2026-01-23T13:52:37.283891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:23.441939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:26.228850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:28.436987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:31.233709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:33.224612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:35.413958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:37.533949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:23.683699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:26.610078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:28.737343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:31.567551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:33.523791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:35.668131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:37.829032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:24.412107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:26.928124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:29.014536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:31.862581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:33.816189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:35.930572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:38.123887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:25.026934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:27.244462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:30.006002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:32.106202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:34.153456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:36.140486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:38.398714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:25.348772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:27.576146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:30.305863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:32.374701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:34.465565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:36.437839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:38.742978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:25.638349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:27.893686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:30.643437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:32.686809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:34.785963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:36.716410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:39.223858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:25.948452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:28.166761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:30.937359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:32.966364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:35.167287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T13:52:37.034380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-23T13:52:54.655946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
brandcategorycolorcurrent_pricecustomer_ratingdayday_namediscount_flagis_returnedmarkdown_percentagemonthoriginal_priceout_of_stockrating_categoryreturn_reasonseasonsizestock_quantityyear
brand1.0000.0390.0200.0090.0170.0340.0070.0100.0380.0000.0330.0180.0570.0260.0000.0580.0000.0000.024
category0.0391.0000.0170.3570.0000.0000.0090.0800.0000.0600.0000.4050.0150.0000.0000.0000.3950.0000.000
color0.0200.0171.0000.0000.0210.0000.0000.0000.0000.0000.0360.0130.0420.0000.0000.0210.0000.0000.084
current_price0.0090.3570.0001.0000.0030.0020.0000.2530.000-0.234-0.0120.9120.0000.0000.0000.0460.173-0.0200.041
customer_rating0.0170.0000.0210.0031.000-0.0240.0000.0000.000-0.019-0.0470.0080.0000.9330.0150.0000.015-0.0160.044
day0.0340.0000.0000.002-0.0241.0000.3690.0440.000-0.013-0.209-0.0060.0270.0170.0000.5370.0470.0350.552
day_name0.0070.0090.0000.0000.0000.3691.0000.0000.0130.0300.3690.0230.0770.0000.0250.5180.0400.0340.526
discount_flag0.0100.0800.0000.2530.0000.0440.0001.0000.0000.9260.0000.1300.0000.0000.0000.0000.0730.0260.000
is_returned0.0380.0000.0000.0000.0000.0000.0130.0001.0000.0520.0000.0000.0000.0000.9990.0470.0000.0310.000
markdown_percentage0.0000.0600.000-0.234-0.019-0.0130.0300.9260.0521.0000.0190.1090.0000.0520.0540.0280.022-0.0040.058
month0.0330.0000.036-0.012-0.047-0.2090.3690.0000.0000.0191.000-0.0120.0000.0340.0000.5450.000-0.0040.909
original_price0.0180.4050.0130.9120.008-0.0060.0230.1300.0000.109-0.0121.0000.0000.0250.0050.0000.187-0.0200.005
out_of_stock0.0570.0150.0420.0000.0000.0270.0770.0000.0000.0000.0000.0001.0000.0230.0000.0580.0000.3940.000
rating_category0.0260.0000.0000.0000.9330.0170.0000.0000.0000.0520.0340.0250.0231.0000.0000.0000.0360.0000.026
return_reason0.0000.0000.0000.0000.0150.0000.0250.0000.9990.0540.0000.0050.0000.0001.0000.0330.0000.0000.015
season0.0580.0000.0210.0460.0000.5370.5180.0000.0470.0280.5450.0000.0580.0000.0331.0000.0360.0330.585
size0.0000.3950.0000.1730.0150.0470.0400.0730.0000.0220.0000.1870.0000.0360.0000.0361.0000.0000.000
stock_quantity0.0000.0000.000-0.020-0.0160.0350.0340.0260.031-0.004-0.004-0.0200.3940.0000.0000.0330.0001.0000.000
year0.0240.0000.0840.0410.0440.5520.5260.0000.0000.0580.9090.0050.0000.0260.0150.5850.0000.0001.000

Missing values

2026-01-23T13:52:40.042985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-23T13:52:40.826705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

product_idcategorybrandseasonsizecolororiginal_pricemarkdown_percentagecurrent_pricepurchase_datestock_quantitycustomer_ratingis_returnedreturn_reasonyearmonthdayday_nameout_of_stockdiscount_flagrating_category
0FB000001OuterwearZaraSpringXLRed196.010.0196.012025-07-05373.000000FalseNo Return202575Saturday00Medium
1FB000002TopsUniqloWinterLPink119.640.0119.642025-08-0622.500000FalseNo Return202586Wednesday00Medium
2FB000003AccessoriesUniqloWinterUnknownBlack33.800.033.802025-08-06224.300000FalseNo Return202586Wednesday00High
3FB000004ShoesUniqloSpringXLBlack75.360.075.362025-07-07482.600000FalseNo Return202577Monday00Medium
4FB000005TopsBanana RepublicWinterXLBlack105.020.0105.022025-08-06102.986604FalseNo Return202586Wednesday00Medium
5FB000006AccessoriesZaraFallUnknownWhite35.030.022.632025-08-06382.900000TrueColor Mismatch202586Wednesday00Medium
6FB000007BottomsZaraSpringSGray53.830.024.492024-10-29382.600000FalseNo Return20241029Tuesday00Medium
7FB000008DressesMangoWinterXLBlue91.5522.171.322025-08-06471.600000FalseNo Return202586Wednesday01Low
8FB000009OuterwearUniqloSpringXLGreen227.7312.8198.582025-03-24142.986604FalseNo Return2025324Monday01Medium
9FB000010OuterwearH&MFallXXLPink248.4110.9221.332025-08-06372.986604FalseNo Return202586Wednesday01Medium
product_idcategorybrandseasonsizecolororiginal_pricemarkdown_percentagecurrent_pricepurchase_datestock_quantitycustomer_ratingis_returnedreturn_reasonyearmonthdayday_nameout_of_stockdiscount_flagrating_category
2166FB002167ShoesBanana RepublicWinterMBrown116.1516.596.992025-08-06113.0FalseNo Return202586Wednesday01Medium
2167FB002168ShoesH&MWinterXSWhite98.220.098.222025-08-06273.3FalseNo Return202586Wednesday00Medium
2168FB002169ShoesAnn TaylorWinterUnknownBeige66.580.066.582025-08-06453.4FalseNo Return202586Wednesday00Medium
2169FB002170AccessoriesUniqloSpringUnknownBeige69.220.069.222024-09-05423.8FalseNo Return202495Thursday00Medium
2170FB002171TopsUniqloWinterXLBlack85.010.085.012025-08-06394.3FalseNo Return202586Wednesday00High
2171FB002172TopsMangoSummerXLBlack31.410.031.412025-08-06102.1FalseNo Return202586Wednesday00Medium
2172FB002173DressesBanana RepublicWinterLBrown50.580.050.582025-08-0613.1TrueChanged Mind202586Wednesday00Medium
2173FB002174AccessoriesBanana RepublicWinterUnknownBeige79.3754.835.882025-08-06402.6FalseNo Return202586Wednesday01Medium
2174FB002175BottomsAnn TaylorSummerXLPink105.520.0105.522025-08-06204.9TrueSize Issue202586Wednesday00High
2175FB002176ShoesAnn TaylorSummerXXLWhite154.020.0154.022025-08-06231.3FalseNo Return202586Wednesday00Low